fix(tokenizer): Kimi-K3 prompt segments and skip_special_tokens in the tiktoken backend

[syuoni]

Description

Problem

Kimi-K3 prompts and responses served through smg's gRPC path diverged from the checkpoint's own tokenization (tokenization_kimi.py + encoding_k3.py, which is also what vLLM and SGLang serve) in two ways:

1. TiktokenTokenizer::decode ignored skip_special_tokens. Every special token in the generated ids was rendered into content, so a request whose stop is not the EOS id (ignore_eos, custom stops) received the <|end_of_msg|> terminator as text on every turn. A client that feeds that text back as history sends the model a control token it never meant to.
2. The K3 renderer built one flat string and the tiktoken backend encoded it with all special tokens enabled. Any marker string inside message text (an earlier assistant turn's <|open|>think<|sep|>..., a user quoting a control token) became a control id, and adjacent attribute pieces such as =".hidden" were BPE-merged across the boundary. The reference build_chat_segments + _encode_chat_segments encode a list of EncodeSegment { text, allow_special }: structural markers with special tokens allowed, every other piece (tag names, attribute pieces, message text of every role, reasoning, tool arguments) as ordinary BPE, each piece on its own.

Solution

• parse_added_tokens_decoder keeps the special flag; decode strips exactly the special: true ids when asked. The special: false markers <|open|>/<|close|>/<|sep|> stay in the text, which is the HuggingFace definition of the flag.
• PromptSegment { text, allow_special }, Encoder::encode_segments, Tokenizer::apply_chat_template_segments, with defaults that reproduce the flat behavior for HF/Jinja/mock backends. The K3 renderer emits the reference's _control/_text segments one for one; the flat apply_kimi_k3_xtml* entry points are their concatenation, so the existing golden fixtures are unchanged. The tiktoken backend encodes text segments with encode_ordinary. Media anchors (<|media_pad|>) inside content remain control segments for prompt expansion.
• Gateway: ProcessedMessages gains segments; the chat and messages preparation stages encode them (encode_segments_blocking, same offload policy). text stays as the joined string for logs and original_text.

Changes

• crates/tokenizer/src/tiktoken.rs: skip_token_ids, decode filter, encode_segments, apply_chat_template_segments for the K3 renderer.
• crates/tokenizer/src/traits.rs, lib.rs: PromptSegment, join_segments, the two trait hooks with defaults.
• crates/tokenizer/src/encoders/kimi_k3_xtml.rs: segment emitters; render_kimi_k3_xtml_segments[_with_effort_default].
• crates/tokenizer/src/cache/mod.rs: forwards; a single control segment still goes through L0/L1, mixed segments are encoded uncached by the inner tokenizer.
• model_gateway/src/routers/grpc/{mod.rs,utils/chat_utils.rs,utils/message_utils.rs,utils/mod.rs,regular/stages/chat/preparation.rs,regular/stages/messages/preparation.rs}: segment plumbing.
• Tests: tiktoken decode/skip and segment encoding; K3 renderer segment structure; tests/kimi_k3_renderer.rs::segment_encoding_matches_vendor_token_ids with tests/fixtures/kimi_k3/k3_render_ids_fixtures.json (ids recorded from the checkpoint's apply_chat_template(tokenize=True) via transformers with trust_remote_code).

Known follow-up: K3 prompts are now encoded piecewise and bypass the L0/L1 tokenizer cache; a cache-aware segment path is a natural next step.

Test Plan

Unit and integration tests (aarch64, in the TokenSpeed runner container):

$ cargo +nightly fmt --all -- --check
(no output)

$ cargo clippy --workspace --all-targets --features smg/vendored-openssl -- -D warnings
    Finished `dev` profile [unoptimized + debuginfo] target(s) in 5m 41s

$ cargo test -p llm-tokenizer
     Running unittests src/lib.rs
test result: ok. 187 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.33s
     Running tests/chat_template_format_detection.rs
test result: ok. 16 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s
     Running tests/chat_template_integration.rs
test result: ok. 13 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s
     Running tests/chat_template_loading.rs
test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s
     Running tests/deepseek_renderer_detection.rs
test result: ok. 15 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s
     Running tests/kimi_k25_renderer_detection.rs
test result: ok. 6 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.01s
     Running tests/kimi_k3_renderer.rs
test result: ok. 11 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.01s
     Running tests/qwen2_vocab_merges.rs
test result: ok. 4 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s
     Running tests/qwen3_asr_bpe_parity.rs
test result: ok. 0 passed; 0 failed; 1 ignored; 0 measured; 0 filtered out; finished in 0.00s
     Running tests/tiktoken_integration.rs
test result: ok. 0 passed; 0 failed; 12 ignored; 0 measured; 0 filtered out; finished in 0.00s
     Running tests/tokenizer_cache_correctness_test.rs
test result: ok. 2 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 1.23s
     Running tests/tokenizer_integration.rs
test result: ok. 16 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 1.04s

$ cargo test -p smg --lib --features vendored-openssl
test result: ok. 1924 passed; 0 failed; 5 ignored; 0 measured; 0 filtered out; finished in 11.18s

Reference parity against the real checkpoint (nvidia/Kimi-K3-NVFP4, same tokenizer files as moonshotai/Kimi-K3). The fixture holds the ids the checkpoint's apply_chat_template(tokenize=True) produces for five prompts: plain user turn, system + assistant history with reasoning, marker strings injected into user and assistant text, a tool call round trip with a marker in the tool result, and a name=".hidden" attribute. The test asserts the segment encoding equals those ids for all five, and that the flat encoding differs exactly for the three cases with markers or the punctuation attribute:

KIMI_K3_MODEL_DIR=<snapshot dir> cargo test -p llm-tokenizer --test kimi_k3_renderer segment_encoding_matches_vendor_token_ids -- --nocapture
test segment_encoding_matches_vendor_token_ids ... ok

Before/after for the decode fix, K3 config ([EOS] and <|end_of_msg|> are special: true, <|open|> is special: false), decode(ids, skip_special_tokens=true):

• before: a[EOS]<|open|>b (flag ignored)
• after: a<|open|>b

covered by tiktoken::tests::test_skip_special_tokens_drops_only_special_flagged_ids and test_skip_special_tokens_holds_text_in_incremental_decode.

Checklist

• cargo +nightly fmt passes
• cargo clippy --all-targets --all-features -- -D warnings passes (run with --features smg/vendored-openssl instead of --all-features: the build host has no system OpenCV for opencv-video)
• (Optional) Documentation updated
• (Optional) Please join us on Slack #sig-smg to discuss, review, and merge PRs

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📥 Commits

Reviewing files that changed from the base of the PR and between 811ab29 and 95f68ec.

📒 Files selected for processing (3)

• crates/tokenizer/src/tiktoken.rs
• crates/tokenizer/tests/kimi_k3_renderer.rs
• model_gateway/src/routers/grpc/utils/chat_utils.rs

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📝 Summary

Summary by CodeRabbit

• New Features

  • Added segmented prompt processing, preserving control markers separately from regular text.
  • Improved Kimi-K3 chat template handling, including media markers and tool-call formatting.
  • Added support for recognizing segmented prompts during tokenization.
  • Added accurate filtering of special tokens during decoding.

• Bug Fixes

  • Corrected handling of special markers and incremental decoding.
  • Improved Kimi-K3 token ID parity with reference behavior.

• Tests

  • Added comprehensive Kimi-K3 rendering fixtures and end-to-end validation.

Walkthrough

The tokenizer API now supports control and text prompt segments. Kimi-K3 renders segmented prompts, Tiktoken applies selective special-token handling, and the model gateway carries segments through chat preparation and tokenization.

Changes

Segmented prompt encoding

Layer / File(s)	Summary
Segment contracts and default APIs crates/tokenizer/src/traits.rs, crates/tokenizer/src/lib.rs	Adds PromptSegment, join_segments, and default segmented tokenizer methods.
Kimi-K3 segmented rendering crates/tokenizer/src/encoders/kimi_k3_xtml.rs, crates/tokenizer/tests/fixtures/kimi_k3/*, crates/tokenizer/tests/kimi_k3_renderer.rs	Kimi-K3 emits structural control segments and text segments. Media anchors become control segments. Renderer tests and token-ID fixtures cover conversations, tools, markers, and attributes.
Tokenizer backend behavior crates/tokenizer/src/tiktoken.rs	Tiktoken encodes segments with selective special-token recognition and filters flagged special IDs during decode.
Cached tokenizer integration crates/tokenizer/src/cache/mod.rs	CachedTokenizer supports segmented encoding and segmented template delegation.
Gateway segmented tokenization model_gateway/src/routers/grpc/*	Processed messages retain segments and joined text. Chat preparation encodes segments through the blocking helper.

Estimated code review effort: 4 (Complex) | ~45 minutes

Merge Risk: ⚪ Minimal · up to 95f68

This update corrects Kimi-K3 segmented prompt encoding and special-token decoding behavior while preserving flat rendered text. No concrete merge-blocking risk remains.

Sequence Diagram(s)

sequenceDiagram
  participant ChatUtils
  participant KimiK3Renderer
  participant TiktokenTokenizer
  ChatUtils->>KimiK3Renderer: apply_chat_template_segments
  KimiK3Renderer-->>ChatUtils: PromptSegment list
  ChatUtils->>TiktokenTokenizer: encode_segments
  TiktokenTokenizer-->>ChatUtils: Encoding

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🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 57.81% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 64 functions across 12 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly identifies both primary changes: Kimi-K3 prompt segmentation and skip_special_tokens handling in the tiktoken backend.
Description check	✅ Passed	The description directly explains the problems, implementation, affected components, tests, and known follow-up for the changeset.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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Actionable comments posted: 2

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Inline comments:
In `@crates/tokenizer/src/tiktoken.rs`:
- Line 244: Update TiktokenTokenizer::new to initialize skip_token_ids from the
selected model’s built-in special-token mapping, including the endoftext token
for Cl100kBase, so decode(..., true) removes built-in special tokens. Add a
regression test constructing TiktokenTokenizer with TiktokenModel::Cl100kBase
and verifying <|endoftext|> is skipped during decoding.

In `@model_gateway/src/routers/grpc/utils/chat_utils.rs`:
- Line 565: Update the continue_final_message handling around
apply_chat_template_segments so flat Tiktoken rendering keeps the rendered
prompt and prefix in one encoding unit, preserving BPE merges and token IDs; use
a renderer-specific path if needed. Add a regression test covering a
Jinja/Tiktoken renderer.

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Configuration used: Organization UI

Review profile: CHILL

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Run ID: 23dc4ba4-b072-46d7-94e1-bd5358f3b819

📥 Commits

Reviewing files that changed from the base of the PR and between 2358af8 and 811ab29.

📒 Files selected for processing (13)

• crates/tokenizer/src/cache/mod.rs
• crates/tokenizer/src/encoders/kimi_k3_xtml.rs
• crates/tokenizer/src/lib.rs
• crates/tokenizer/src/tiktoken.rs
• crates/tokenizer/src/traits.rs
• crates/tokenizer/tests/fixtures/kimi_k3/k3_render_ids_fixtures.json
• crates/tokenizer/tests/kimi_k3_renderer.rs
• model_gateway/src/routers/grpc/mod.rs
• model_gateway/src/routers/grpc/regular/stages/chat/preparation.rs
• model_gateway/src/routers/grpc/regular/stages/messages/preparation.rs
• model_gateway/src/routers/grpc/utils/chat_utils.rs
• model_gateway/src/routers/grpc/utils/message_utils.rs
• model_gateway/src/routers/grpc/utils/mod.rs

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[CatherineSue]

segments is not applied to all models. Why are we updating the APIs here?